PROSIGN: a method for protein secondary structure assignment based on three-dimensional coordinates of consecutive C(alpha) atoms

The automatic assignment of secondary structure from three-dimensional atomic coordinates of proteins is an essential step for the analysis and modeling of protein structures. So different methods based on different criteria have been designed to perform this task. We introduce a new method for protein secondary structure assignment based solely on C(alpha) coordinates. We introduce four certain relations between C(alpha) three-dimensional coordinates of consecutive residues, each of which applies to one of the four regular secondary structure categories: alpha-helix, 3(10)-helix, pi-helix and beta-strand. In our approach, the deviation of the C(alpha) coordinates of each residue from each relation is calculated. Based on these deviation values, secondary structures are assigned to all residues of a protein. We show that our method agrees well with popular methods as DSSP, STRIDE and assignments in PDB files. It is shown that our method gives more information about helix geometry leading to more accurate secondary structure assignment.     

SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/     

NMR Spectroscopic Assignment of Backbone and Side‐Chain Protons in Fully Protonated Proteins: Microcrystals,Sedimented Assemblies,and Amyloid Fibrils

We demonstrate sensitive detection of alpha protons of fully protonated proteins by solid‐state NMR spectroscopy with 100–111 kHz magic‐angle spinning (MAS). The excellent resolution in the Cα‐Hα plane is demonstrated for 5 proteins, including microcrystals, a sedimented complex, a capsid and amyloid fibrils. A set of 3D spectra based on a Cα–Hα detection block was developed and applied for the sequence‐specific backbone and aliphatic side‐chain resonance assignment using only 500 μg of sample. These developments accelerate structural studies of biomolecular assemblies available in submilligram quantities without the need of protein deuteration.     

Protein secondary structure prediction with SPARROW

A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used. Such functions discriminate between two specific structural classes or between a single specific class and the rest. The approach uses a hierarchical scheme of scoring functions and a neural network. The parameters are determined by optimizing the recall of learning data. Quality control is performed by predicting separate independent test data. A first set of scoring functions is trained to correlate the secondary structures of residues with profiles of sequence windows of width 15, centered at these residues. The sequence profiles are obtained by multiple sequence alignment with PSI-BLAST. A second set of scoring functions is trained to correlate the secondary structures of the center residues with the secondary structures of all other residues in the sequence windows used in the first step. Finally, a neural network is trained using the results from the second set of scoring functions as input to make a decision on the secondary structure class of the residue in the center of the sequence window. Here, we consider the three-class problem of helix, strand, and other secondary structures. The corresponding prediction scheme "SPARROW" was trained with the ASTRAL40 database, which contains protein domain structures with less than 40% sequence identity. The secondary structures were determined with DSSP. In a loose assignment, the helix class contains all DSSP helix types (α, 3-10, π), the strand class contains β-strand and β-bridge, and the third class contains the other structures. In a tight assignment, the helix and strand classes contain only α-helix and β-strand classes, respectively. A 10-fold cross validation showed less than 0.8% deviation in the fraction of correct structure assignments between true prediction and recall of data used for training. Using sequences of 140,000 residues as a test data set, 80.46% ± 0.35% of secondary structures are predicted correctly in the loose assignment, a prediction performance, which is very close to the best results in the field. Most applications are done with the loose assignment. However, the tight assignment yields 2.25% better prediction performance. With each individual prediction, we also provide a confidence measure providing the probability that the prediction is correct. The SPARROW software can be used and downloaded on the Web page http://agknapp.chemie.fu-berlin.de/sparrow/ .     

The effect of diamine length on the direct solid state polycondensation of semi‐aromatic nylon salts

In the current paper, a comparative study on the direct solid state polycondensation (DSSP) reaction of different terephthalate based semi‐aromatic salts (XT salts, X = 4–18) in the TGA micro‐reactor is reported. High purity XT salts were prepared in solution and were used as starting materials for DSSP. The reaction temperature (T_DSSP) for each salt was suitably selected as 20 °C–30 °C below the melting point T_m of the respective salt. The PAXT products were characterized by TGA/DSC, liquid ¹H‐NMR, and SEM. In the DSSP of XT salts, some diamine is always lost to the gas phase and as a consequence, the attainable molecular weight of the polymer formed gets limited by the unbalance of acid and amine end‐groups. The TGA curves show that as the diamine length increases and its volatility decreases, higher molecular weights are obtained. SEM pictures of the products reveal true solid character during the polymerization reaction up to and including PA10T, whereas PA5T, PA12T, and PA18T reveal stickiness and agglomeration during reaction. A possible mechanism explaining such behaviour is also provided. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2016 , 54, 2493–2506     

Impaired Chaperone Activity of Human Heat Shock Protein Hsp27 Site‐Specifically Modified with Argpyrimidine

Non‐enzymatic posttranslational modifications (nPTMs) affect at least ～30 % of human proteins, but our understanding of their impact on protein structure and function is limited. Studies of nPTMs are difficult because many modifications are not included in common chemical libraries or protein expression systems and should be introduced site‐specifically. Herein, we probed the effect of the nPTM argpyrimidine on the structure and function of human protein Hsp27, which acquires argpyrimidine at residue 188 in vivo. We developed a synthetic approach to an argpyrimidine building block, which we then incorporated at position 188 of Hsp27 through protein semisynthesis. This modification did not affect the protein secondary structure, but perturbed the oligomeric assembly and impaired chaperone activity. Our work demonstrates that protein function can be altered by a single nPTM and opens up a new area of investigation only accessible by methods that allow site‐selective protein modification.     

Water‐Mediated Synthesis of a Superionic Halide Solid Electrolyte

To promote the development of solid‐state batteries, polymer‐, oxide‐, and sulfide‐based solid‐state electrolytes (SSEs) have been extensively investigated. However, the disadvantages of these SSEs, such as high‐temperature sintering of oxides, air instability of sulfides, and narrow electrochemical windows of polymers electrolytes, significantly hinder their practical application. Therefore, developing SSEs that have a high ionic conductivity (>10^?3 S cm^?1), good air stability, wide electrochemical window, excellent electrode interface stability, low‐cost mass production is required. Herein we report a halide Li⁺ superionic conductor, Li₃InCl₆, that can be synthesized in water. Most importantly, the as‐synthesized Li₃InCl₆ shows a high ionic conductivity of 2.04×10^?3 S cm^?1 at 25 °C. Furthermore, the ionic conductivity can be recovered after dissolution in water. Combined with a LiNi_0.8Co_0.1Mn_0.1O₂ cathode, the solid‐state Li battery shows good cycling stability.     

ProteinUnet—An efficient alternative to SPIDER3‐single for sequence‐based prediction of protein secondary structures

Predicting protein function and structure from sequence remains an unsolved problem in bioinformatics. The best performing methods rely heavily on evolutionary information from multiple sequence alignments, which means their accuracy deteriorates for sequences with a few homologs, and given the increasing sequence database sizes requires long computation times. Here, a single‐sequence‐based prediction method is presented, called ProteinUnet, leveraging an U‐Net convolutional network architecture. It is compared to SPIDER3‐Single model, based on long short‐term memory‐bidirectional recurrent neural networks architecture. Both methods achieve similar results for prediction of secondary structures (both three‐ and eight‐state), half‐sphere exposure, and contact number, but ProteinUnet has two times fewer parameters, 17 times shorter inference time, and can be trained 11 times faster. Moreover, ProteinUnet tends to be better for short sequences and residues with a low number of local contacts. Additionally, the method of loss weighting is presented as an effective way of increasing accuracy for rare secondary structures.     

New energy terms for reduced protein models implemented in an off‐lattice force field

Parameterization and test calculations of a reduced protein model with new energy terms are presented. The new energy terms retain the steric properties and the most significant degrees of freedom of protein side chains in an efficient way using only one to three virtual atoms per amino acid residue. The energy terms are implemented in a force field containing predefined secondary structure elements as constraints, electrostatic interaction terms, and a solvent‐accessible surface area term to include the effect of solvation. In the force field the main‐chain peptide units are modeled as electric dipoles, which have constant directions in α‐helices and β‐sheets and variable conformation‐dependent directions in loops. Protein secondary structures can be readily modeled using these dipole terms. Parameters of the force field were derived using a large set of experimental protein structures and refined by minimizing RMS errors between the experimental structures and structures generated using molecular dynamics simulations. The final average RMS error was 3.7 Å for the main‐chain virtual atoms (C_α atoms) and 4.2 Å for all virtual atoms for a test set of 10 proteins with 58–294 amino acid residues. The force field was further tested with a substantially larger test set of 608 proteins yielding somewhat lower accuracy. The fold recognition capabilities of the force field were also evaluated using a set of 27,814 misfolded decoy structures. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 1229–1242, 2001     

UCST‐type phase transition driven by protein‐derived polypeptide employing gelatin and chitosan

Here, we describe the thermosensitive reversible phase transition behaviors of polyelectrolyte complex composed of gelatin and chitosan (G/C complex). An aqueous dispersion of the G/C complexes showed a clear upper critical solution temperature (UCST) at around 30°C. The thermosensitive phase transition behavior showed excellent reversibility and large thermal hysteresis as a usual phenomenon based on the intra‐ and inter‐molecular interaction change. A high correlation was observed between the UCST of the G/C complex and the helix‐melting temperature of gelatin by circular dichroism, which suggested that the phase transition of the G/C complex corresponded to the secondary structure (helix‐coil) transition of gelatin. Notably, the UCST of the G/C complex shifted to lower temperatures in the presence of urea, which is well known to destabilize gelatin, whereas the addition of salt led to the dissolution of the G/C complex. It is envisaged that the results of this study will have a significant impact on the fabrication of UCST‐type thermosensitive materials, which can be utilized under aqueous physiological conditions using well‐known biopolymers. This protein‐derived functional material, which responds to the secondary structure transition, could also be used for the development of novel UCST‐type thermosensitive biomaterials. Copyright © 2017 John Wiley & Sons, Ltd.     

A fast method for large‐scale De Novo peptide and miniprotein structure prediction

Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large‐scale experiments is still missing. We introduce a new approach—PEP‐FOLD—to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model‐derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse‐grained energy score. On a benchmark of 52 peptides with 9–23 amino acids, PEP‐FOLD generates lowest‐energy conformations within 2.8 and 2.3 Å Cα root‐mean‐square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27–49 amino acids, PEP‐FOLD reaches an accuracy of 3.6 and 4.6 Å Cα root‐mean‐square deviation for the most‐native and lowest‐energy conformations, using the nonflexible regions identified by NMR. PEP‐FOLD simulations are fast—a few minutes only—opening therefore, the door to in silico large‐scale rational design of new bioactive peptides and miniproteins. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Charge‐Induced Secondary Structure Transformation of Amyloid‐Derived Dipeptide Assemblies from β‐Sheet to α‐Helix

Secondary structures such as α‐helix and β‐sheet are the major structural motifs within the three‐dimensional geometry of proteins. Therefore, structure transitions from β‐sheet to α‐helix not only can serve as an effective strategy for the therapy of neurological diseases through the inhibition of β‐sheet aggregation but also extend the application of α‐helix fibrils in biomedicine. Herein, we present a charge‐induced secondary structure transition of amyloid‐derived dipeptide assemblies from β‐sheet to α‐helix. We unravel that the electrostatic (charge) repulsion between the C‐terminal charges of the dipeptide molecules are responsible for the conversion of the secondary structure. This finding provides a new perspective to understanding the secondary structure formation and transformation in the supramolecular organization and life activity.     

Site‐Resolved Backbone and Side‐Chain Intermediate Dynamics in a Carbohydrate‐Binding Module Protein Studied by Magic‐Angle Spinning NMR Spectroscopy

Magic‐angle spinning solid‐state NMR spectroscopy has been applied to study the dynamics of CBM3b–Cbh9A from Clostridium thermocellum (ctCBM3b), a cellulose binding module protein. This 146‐residue protein has a nine‐stranded β‐sandwich fold, in which 35 % of the residues are in the β‐sheet and the remainder are composed of loops and turns. Dynamically averaged ¹H‐¹³C dipolar coupling order parameters were extracted in a site‐specific manner by using a pseudo‐three‐dimensional constant‐time recoupled separated‐local‐field experiment (dipolar‐chemical shift correlation experiment; DIPSHIFT). The backbone‐Cα and Cβ order parameters indicate that the majority of the protein, including turns, is rigid despite having a high content of loops; this suggests that restricted motions of the turns stabilize the loops and create a rigid structure. Water molecules, located in the crystalline interface between protein units, induce an increased dynamics of the interface residues thereby lubricating crystal water‐mediated contacts, whereas other crystal contacts remain rigid.     

Use of Molecular‐Dynamics Simulation for Optimizing Protein Stability: Consensus‐Designed Ankyrin Repeat Proteins

In earlier work, two highly homologous (87% sequence identity) ankyrin repeat (AR) proteins, E3_5 and E3_19, were studied using molecular‐dynamics (MD) simulation. Their stabilities were compared, and it was found that the C‐terminal capping unit is unstable in the protein E3_19, in agreement with CD experiments. The different stabilities of these two very similar proteins could be explained by the different charge distributions among the AR units of the two proteins. Here, another AR protein, N3C, with yet another charge distribution has been simulated using MD, and its stability was analyzed. In agreement with the experimental data, the structure of N3C was found to be less stable than that of E3_5, but, in contrast to E3_19, secondary structure was only slightly lost, while structurally N3C is closer to E3_19 than to E3_5. The results suggest that a homogeneous charge distribution over the repeat units does enhance the stability of design AR proteins in aqueous solution, which, however, may be modulated by the bulkiness of amino‐acid side chains involved in the mutations.     

Analysis of molecular structures and mechanisms for toxins derived from venomous animals

As predominant component in the venom of many dangerous animal species, toxins have been thoroughly investigated for drug design or as pharmacologic tools. The present study demonstrated the use of size and hydrophobicity of amino acid residues for the purposes of quantifying the valuable sequence–structure relationship and performing further analysis of interactional mechanisms in secondary structure elements (SSEs) for toxin native conformations. First, we showed that the presence of large and hydrophobic residues varying in availability in the primary sequences correspondingly affects the amount of these residues being used in the SSEs in accordance with linear behavioral patterns from empirical assessments of experimentally derived toxins and non-toxins. Subsequent derivation of prediction rules was established with the aim of analyzing molecular structures and mechanisms by means of 114 residue compositions for venom toxins. The obtained results concerning the linear behavioral patterns demonstrated the nature of the information transfer occurring from the primary to secondary structures. A dual action mechanism was established, taking into account steric and hydrophobic interactions. Finally, a new residue composition prediction method for SSEs of toxins was suggested.     

Synthesis and Characterisation of Self‐Assembled and Self‐Adjuvanting Asymmetric Multi‐Epitope Lipopeptides of Ovalbumin

Designing a lipopeptide (LP) vaccine with a specific asymmetric arrangement of epitopes may result in an improved display of antigens, increasing host‐cell recognition and immunogenicity. This study aimed to synthesise and characterise the physicochemical properties of a library of asymmetric LP‐based vaccine candidates that contained multiple CD4⁺ and CD8⁺ T‐cell epitopes from the model protein antigen, ovalbumin. These fully synthetic vaccine candidates were prepared by microwave‐assisted solid phase peptide synthesis. The C12 or C16 lipoamino acids were coupled to the N or C terminus of the OVA CD4 peptide epitope. The OVA CD4 LPs and OVA CD8 peptide constructs were then conjugated using azide–alkyne Huisgen cycloaddition to give multivalent synthetic vaccines. Physiochemical characterisation of these vaccines showed a tendency to self‐assemble in aqueous media. Changes in lipid length and position induced self‐assembly with significant changes to their morphology and secondary structure as shown by transmission electron microscopy and circular dichroism.     

Structural characterization of intact antibodies by high‐resolution LTQ Orbitrap mass spectrometry

Recombinant monoclonal antibodies (MAbs) can be heterogeneous due to modifications that can occur during expression, purification or during storage. These large multichain proteins (～150 kDa) are structurally challenging for detailed characterization to identify the sites of modifications. We report the use of LTQ Orbitrap mass spectrometry to accurately measure the average masses of individual glycoforms by direct infusion of an intact antibody. To identify the site‐specific modification of methionines in the antibody caused by forced oxidation, we used a ‘middle‐down’ approach. The antibody was subjected to limited digestion using the endoproteinase Lys‐C and reduced to generate Fab heavy chain, single chain Fc and light chain fragments (～25 kDa each). These species were subjected to on‐line liquid chromatography/mass spectrometry/mass spectrometry (LC/MS/MS) analysis using an LTQ Orbitrap, where these large precursors were dissociated by higher‐energy collisions in the C‐trap. High resolution and accuracy achieved for resulting fragments allowed us to show in a site‐specific manner that only the methionines in the Fc heavy chain were oxidized under the studied conditions. Copyright © 2009 John Wiley & Sons, Ltd.     

Analysis of crystallization behavior and crystal modifications of poly(butylene‐2,6‐naphthalene dicarboxylate)

Information on the crystalline structure and the properties of poly(butylene‐2,6‐naphthalene dicarboxylate) (PBN) has not been well reported until now, but it is known that there are two different crystal modifications in PBN, as follows: one is formed in isotropic samples by annealing (α form); another appears by annealing with tension (β form). The relation between the crystal modifications and the kinetics of isothermal crystallization for PBN was investigated using in‐situ Fourier transform infrared spectroscopy (FTIR) and wide‐angle X‐ray diffraction (WAXD). The melting behavior of each crystalline form was also studied by means of FTIR and differential scanning calorimetry (DSC) measurements. From the analysis of the melt‐crystallized PBN specimens, the two crystalline forms coexisted in the isotropic samples melt‐crystallized at 230°C, but only the α crystal modification was observed in the films annealed at lower temperatures. In addition, it was revealed that, at 230°C, the β modification was formed only in the primary crystallization process. © 1999 John Wiley & Sons, Inc. J Polym Sci B: Polym Phys 37: 561–574, 1999     

Incorporating support vector machine for identifying protein tyrosine sulfation sites

Tyrosine sulfation is a post‐translational modification of many secreted and membrane‐bound proteins. It governs protein‐protein interactions that are involved in leukocyte adhesion, hemostasis, and chemokine signaling. However, the intrinsic feature of sulfated protein remains elusive and remains to be delineated. This investigation presents SulfoSite, which is a computational method based on a support vector machine (SVM) for predicting protein sulfotyrosine sites. The approach was developed to consider structural information such as concerning the secondary structure and solvent accessibility of amino acids that surround the sulfotyrosine sites. One hundred sixty‐two experimentally verified tyrosine sulfation sites were identified using UniProtKB/SwissProt release 53.0. The results of a five‐fold cross‐validation evaluation suggest that the accessibility of the solvent around the sulfotyrosine sites contributes substantially to predictive accuracy. The SVM classifier can achieve an accuracy of 94.2% in five‐fold cross validation when sequence positional weighted matrix (PWM) is coupled with values of the accessible surface area (ASA). The proposed method significantly outperforms previous methods for accurately predicting the location of tyrosine sulfation sites. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009